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Correlation Clustering by Contraction, a More Effective Method

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Book cover Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 655))

Abstract

In this article we propose two effective methods to produce a near optimal solution for the problem of correlation clustering. We study their properties at different circumstances, and show that the inner structure generated by a tolerance relation has effect on the accuracy of the methods. Finally, we show that there is no royal road to the sequence of clusterings.

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Author information

Authors and Affiliations

  1. Faculty of Informatics, University of Debrecen, Debrecen, Hungary

    László Aszalós & Tamás Mihálydeák

Authors
  1. László Aszalós
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  2. Tamás Mihálydeák
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Corresponding author

Correspondence to László Aszalós .

Editor information

Editors and Affiliations

  1. Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Aszalós, L., Mihálydeák, T. (2016). Correlation Clustering by Contraction, a More Effective Method. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-319-40132-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-40132-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40131-7

  • Online ISBN: 978-3-319-40132-4

  • eBook Packages: EngineeringEngineering (R0)

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